Papers
On Matching Pursuit and Coordinate Descent
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy et al.
On Nesting Monte Carlo Estimators
Tom Rainforth, Rob Cornish, Hongseok Yang et al.
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Risi Kondor, Shubhendu Trivedi
On the Implicit Bias of Dropout
Poorya Mianjy, Raman Arora, Rene Vidal
On the Limitations of First-Order Approximation in GAN Dynamics
Jerry Li, Aleksander Madry, John Peebles et al.
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann, Percy Liang
On the Spectrum of Random Features Maps of High Dimensional Data
Zhenyu Liao, Romain Couillet
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri Chatterji, Nicolas Flammarion, Yian Ma et al.
Open Category Detection with PAC Guarantees
Si Liu, Risheek Garrepalli, Thomas Dietterich et al.
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Junhong Lin, Volkan Cevher
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin, Volkan Cevher
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Ganggang Xu, Zuofeng Shang, Guang Cheng
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Mark McLeod, Stephen Roberts, Michael A. Osborne
Optimization Landscape and Expressivity of Deep CNNs
Quynh Nguyen, Matthias Hein
Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Pas et al.
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Pengtao Xie, Wei Wu, Yichen Zhu et al.
Orthogonal Machine Learning: Power and Limitations
Lester Mackey, Vasilis Syrgkanis, Ilias Zadik
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich, Devin Willmott, Qiang Ye
Out-of-sample extension of graph adjacency spectral embedding
Keith Levin, Fred Roosta, Michael Mahoney et al.
Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serra, Didac Suris, Marius Miron et al.
Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni
Parallel Bayesian Network Structure Learning
Tian Gao, Dennis Wei
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin et al.